Task craving MLM analyses
Dani Cosme
2022-10-25
load packages
library(pacman)
pacman::p_load(tidyverse, brms, ggeffects, tidybayes, ROCR, caret, interactions, install = TRUE)
devtools::install_github("hadley/emo")define aesthetics
palette = c("#e64626", "#1985a1", "#4c5c68", "#FAC748")
palette_group = c(palette[2], palette[4])
plot_aes = theme_minimal() +
theme(legend.position = "top",
legend.text = element_text(size = 12),
text = element_text(size = 16, family = "Futura Medium"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.text = element_text(color = "black"),
axis.line = element_line(colour = "black"),
axis.ticks.y = element_blank())define functions
make_table = function(data) {
data %>%
broom.mixed::tidy(conf.int = TRUE) %>%
filter(effect == "fixed") %>%
mutate(term = gsub("\\(Intercept\\)", "intercept", term),
term = gsub("trial_condregulation", "trial condition (regulation)", term),
term = gsub("trial_cond_recode", "trial condition (regulation)", term),
term = gsub("dot_between_std_noc", "signature expression (between)", term),
term = gsub("dot_within_std", "signature expression (within)", term),
term = gsub("dot_sd", "signature expression variability", term),
term = gsub("dist_expression_std", "signature expression modulation", term),
term = gsub("mean_expression", "mean signature expression", term),
term = gsub("regulation_expression", "signature expression (mindfulness)", term),
term = gsub("reactivity_expression", "signature expression (reactivity)", term),
term = gsub("active_weekon", "intervention week (on)", term),
term = gsub("time_of_daymorning", "time of day (morning)", term),
term = gsub("signal_count", "signal", term),
term = gsub("SocialWeekendWeekend", "weekend", term),
term = gsub("gender_f", "gender (female)", term),
term = gsub(":", " x ", term),
`b [95% CI]` = sprintf("%.2f [%.2f, %.2f]", estimate, conf.low, conf.high),) %>%
select(term, `b [95% CI]`) %>%
knitr::kable(digits = 2)
}load and tidy data
- remove trials missing stimulus information
- the following participants had technical errors during all mindfulness trials: sub-MURIP012, sub-MURIP063, sub-MURIP078
- remove trials in which the mean signal intensity of the brain map is +/- 3 SDs from the mean
task
task_data = read.csv("../data/task/cuereact_all062920.csv", stringsAsFactors = FALSE) %>%
filter(grepl("rating", trial_type)) %>%
select(pID, block_num, trial_num, trial_type, resp, rt, stim) %>%
mutate(trial_cond = ifelse(grepl("friend3|friend4", trial_type), "regulation",
ifelse(grepl("friend1|friend2", trial_type), "up-regulation",
ifelse(grepl("mindful", trial_type), "regulation",
ifelse(grepl("nonalc", trial_type), "non-alcohol reactivity",
ifelse(grepl("rating_alc_react", trial_type), "reactivity", NA))))),
trial_cond = factor(trial_cond, levels = c("non-alcohol reactivity", "reactivity", "regulation", "up-regulation"))) %>%
rename("stimulus" = stim) %>%
filter(!is.na(stimulus))
task_conditions = task_data %>%
select(pID, trial_type) %>%
unique() %>%
filter(grepl("friend|mindful", trial_type) | pID %in% c("sub-MURIP012", "sub-MURIP063", "sub-MURIP078")) %>%
mutate(condition = ifelse(grepl("friend", trial_type), "perspective", "mindfulness")) %>%
select(-trial_type) %>%
unique()
task_data_all = task_data %>%
left_join(., task_conditions) %>%
mutate(condition = ifelse(is.na(condition), "control", condition),
trial_cond = factor(trial_cond, c("non-alcohol reactivity", "reactivity", "regulation", "up-regulation")))
task_mindfulness = task_data_all %>%
filter(condition == "mindfulness")dots
file_dir = "../data/dots"
file_pattern = "sub.*"
file_list = list.files(file_dir, pattern = file_pattern)
dots_tmp = data.frame()
for (file in file_list) {
tmp = tryCatch(read.table(file.path(file_dir,file), fill = TRUE, header = TRUE, sep = ",") %>%
extract(file, c("pID", "beta"), ".*(sub-MURIC[0-9]{3}|sub-MURIP[0-9]{3})/(beta_[0-9]{4}).nii") %>%
mutate(stimulus = as.character(stimulus)), error = function(e) message(file))
dots_tmp = rbind(dots_tmp, tmp)
rm(tmp)
}
dots = dots_tmp %>%
left_join(., task_conditions) %>%
mutate(condition = ifelse(is.na(condition), "control", condition)) %>%
group_by(condition) %>%
filter(!stimulus == "nan") %>%
mutate(sd3_signal = 3 * sd(mean_signal, na.rm = TRUE),
grand_mean_signal = mean(mean_signal, na.rm = TRUE),
outlier = ifelse(mean_signal > grand_mean_signal + sd3_signal, 1,
ifelse(mean_signal < grand_mean_signal - sd3_signal, 1, 0))) %>%
select(-sd3_signal, -grand_mean_signal) %>%
filter(!outlier == 1)classifier data
classifier_data = read.csv("../data/mvpa/logistic_stats.csv", stringsAsFactors = FALSE) %>%
rename("pID" = subjectID) %>%
mutate(predicted_factor = ifelse(predicted > .5, "regulate", "react"),
predicted_factor = as.factor(predicted_factor),
actual_factor = ifelse(actual == 1, "regulate", "react"),
actual_factor = as.factor(actual_factor))merge and prep for modeling
- filter out motion exclusions: sub-MURIC303
- filter out reactivity to non-alcoholic beverage trials
- recode trial condition as regulation = -.5 and reactivity = .5 to test relationship collapsed across conditions
- create disaggregated_mindfulness dataset
dot_within_std= within-person centered level-1 signature expression variable; standardized across participantsdot_between_std= grand-mean centered level-2 signature expression variable; standardized across participants
merged = task_data_all %>%
full_join(., dots) %>%
filter(!pID == "sub-MURIC303") %>%
filter(trial_cond %in% c("reactivity", "regulation")) %>%
mutate(trial_cond_recode = ifelse(trial_cond == "regulation", .5, -.5),
trial_cond = as.factor(as.character(trial_cond)))
between = merged %>%
select(pID, dot, trial_cond, trial_cond_recode, condition) %>%
group_by(pID, trial_cond, trial_cond_recode, condition) %>%
summarize(dot_between = mean(dot, na.rm = TRUE)) %>%
group_by(condition) %>%
mutate(sd_dot = sd(dot_between, na.rm = TRUE),
#dot_between_c = scale(dot_between, scale = FALSE, center = TRUE),
#dot_between_std = dot_between_c / sd_dot,
dot_between_std_noc = dot_between / sd_dot) %>%
select(-sd_dot)
within = merged %>%
select(pID, block_num, trial_num, dot, condition, trial_cond) %>%
group_by(pID, condition) %>%
mutate(dot_within_c = scale(dot, scale = FALSE, center = TRUE)) %>%
group_by(condition) %>%
mutate(sd_dot = sd(dot_within_c, na.rm = TRUE),
dot_within_std = dot_within_c / sd_dot) %>%
mutate(classifier_accuracy = ifelse(dot > 0 & trial_cond == "regulation", "hit",
ifelse(dot < 0 & trial_cond == "reactivity", "correct rejection",
ifelse(dot > 0 & trial_cond == "reactivity", "false alarm",
ifelse(dot < 0 & trial_cond == "regulation", "miss", NA))))) %>%
select(-sd_dot)
disaggregated = merged %>%
left_join(., between, by = c("pID", "condition", "trial_cond", "trial_cond_recode")) %>%
left_join(., within, by = c("pID", "condition", "trial_num", "block_num", "trial_cond", "dot")) %>%
filter(trial_cond %in% c("reactivity", "regulation"))
disaggregated_mindfulness = disaggregated %>%
filter(condition == "mindfulness")
disaggregated_control = disaggregated %>%
filter(condition == "control")preregistered hypotheses
mindfulness signature development analyses
Here is the weight map from the MVPA analyses:
H1a
We expect that we will be able to train a classifier at the run level to distinguish mindfulness from uninstructed reactivity to alcohol cues with greater than chance accuracy decoding.
Results
✅ Average cross-validation decoding accuracy is greater than chance (though there’s lot of room for improvement).
ROC
classifier_data %>%
do({
condition = .$condition
pred = prediction(.$predicted, .$actual)
perf = performance(pred, measure = "tpr", x.measure = "fpr")
data.frame(cut = perf@alpha.values[[1]],fpr = perf@x.values[[1]],tpr = perf@y.values[[1]])
}) %>%
ggplot(aes(fpr, tpr)) +
geom_line(color = palette[4], size = 1) +
geom_abline(intercept = 0, slope = 1) +
scale_x_continuous(breaks = seq(0, 1, .2)) +
scale_y_continuous(breaks = seq(0, 1, .2)) +
labs(x = "\nfalse positive rate (1 - specificity)", y = "true positive rate (sensitivity)\n") +
theme(legend.position = c(.8, .3),
legend.spacing.y = unit(-.1, "cm")) +
plot_aes
confusion matrix
caret::confusionMatrix(classifier_data$predicted_factor, classifier_data$actual_factor)## Confusion Matrix and Statistics
##
## Reference
## Prediction react regulate
## react 59 45
## regulate 52 66
##
## Accuracy : 0.5631
## 95% CI : (0.4951, 0.6293)
## No Information Rate : 0.5
## P-Value [Acc > NIR] : 0.03486
##
## Kappa : 0.1261
##
## Mcnemar's Test P-Value : 0.54239
##
## Sensitivity : 0.5315
## Specificity : 0.5946
## Pos Pred Value : 0.5673
## Neg Pred Value : 0.5593
## Prevalence : 0.5000
## Detection Rate : 0.2658
## Detection Prevalence : 0.4685
## Balanced Accuracy : 0.5631
##
## 'Positive' Class : react
## H1b
Given that the classifier is developed at the run level, we will also confirm that the expression of the mindfulness signature is evident at the trial-level. That is, we expect the signature expression to be higher during mindfulness trials compared to reactivity trials.
Results
✅ Signature expression was higher on average during mindfulness trials than reactivity trials. Applying the signature as a classifier on a trial level, we also observed relatively high accuracy (70%) that is significantly above chance.
means
plot
data_means = merged %>%
filter(condition == "mindfulness") %>%
mutate(trial_cond = gsub("regulation", "mindfulness", trial_cond),
trial_cond = factor(trial_cond, levels = c("reactivity", "mindfulness")))
data_means %>%
ggplot(aes(trial_cond, dot, color = trial_cond, fill = trial_cond)) +
stat_summary(fun.data = "mean_cl_boot", geom = "bar") +
stat_summary(fun.data = "mean_cl_boot", geom = "errorbar", width = 0, color = "black") +
scale_color_manual(name = "", values = palette) +
scale_fill_manual(name = "", values = palette) +
labs(x = "\ntrial type", y = "signature expression\n") +
plot_aes +
theme(legend.position = "none")
run model
table
make_table(mod_means)| term | b [95% CI] |
|---|---|
| intercept | -7.18 [-8.04, -6.31] |
| trial_condmindfulness | 13.33 [12.15, 14.52] |
summary
summary(mod_means)## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: dot ~ trial_cond + (1 | pID)
## Data: data_means (Number of observations: 2265)
## Draws: 4 chains, each with iter = 1000; warmup = 500; thin = 1;
## total post-warmup draws = 2000
##
## Group-Level Effects:
## ~pID (Number of levels: 37)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.43 0.32 0.01 1.15 1.00 1119 1124
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## Intercept -7.18 0.44 -8.04 -6.31 1.00 4113
## trial_condmindfulness 13.33 0.62 12.15 14.52 1.00 3650
## Tail_ESS
## Intercept 1158
## trial_condmindfulness 1309
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 14.18 0.21 13.79 14.59 1.00 3349 1293
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).ROC
acc_line1 = data.frame(x = c(0, 1), y = c(1, 1))
acc_line2 = data.frame(y = c(0, 1), x = c(0, 0))
merged %>%
filter(condition %in% c("mindfulness", "perspective")) %>%
filter(!is.na(dot)) %>%
mutate(actual = ifelse(trial_cond == "regulation", 1, 0)) %>%
group_by(condition) %>%
do({
condition = .$condition
pred = prediction(.$dot, .$actual)
perf = performance(pred, measure = "tpr", x.measure = "fpr")
data.frame(cut = perf@alpha.values[[1]],fpr = perf@x.values[[1]],tpr = perf@y.values[[1]])
}) %>%
ggplot(aes(fpr, tpr)) +
geom_line(aes(color = condition), size = 1) +
geom_abline(intercept = 0, slope = 1) +
geom_line(data = acc_line1, aes(x, y)) +
geom_line(data = acc_line2, aes(x, y)) +
scale_color_manual(values = palette_group) +
scale_x_continuous(breaks = seq(0, 1, .2)) +
scale_y_continuous(breaks = seq(0, 1, .2)) +
labs(x = "\nfalse positive rate (1 - specificity)", y = "true positive rate (sensitivity)\n") +
plot_aes +
theme(legend.position = "none",
legend.spacing.y = unit(-.1, "cm"))
confusion matrix
conf_data = merged %>%
filter(condition == "mindfulness") %>%
filter(!is.na(dot)) %>%
mutate(predicted = ifelse(dot > 0, "regulation",
ifelse(dot < 0, "reactivity", NA)),
predicted = as.factor(predicted),
trial_cond = as.factor(as.character(trial_cond)))
caret::confusionMatrix(conf_data$predicted, conf_data$trial_cond)## Confusion Matrix and Statistics
##
## Reference
## Prediction reactivity regulation
## reactivity 836 336
## regulation 348 745
##
## Accuracy : 0.698
## 95% CI : (0.6786, 0.7169)
## No Information Rate : 0.5227
## P-Value [Acc > NIR] : <0.0000000000000002
##
## Kappa : 0.3951
##
## Mcnemar's Test P-Value : 0.6741
##
## Sensitivity : 0.7061
## Specificity : 0.6892
## Pos Pred Value : 0.7133
## Neg Pred Value : 0.6816
## Prevalence : 0.5227
## Detection Rate : 0.3691
## Detection Prevalence : 0.5174
## Balanced Accuracy : 0.6976
##
## 'Positive' Class : reactivity
## craving: behavior only
run model
plot
behavior_mindfulness %>%
spread_draws(b_Intercept, b_trial_condregulation) %>%
mutate(reactivity = b_Intercept,
mindfulness = b_Intercept + b_trial_condregulation) %>%
gather(`trial type`, value, reactivity, mindfulness) %>%
ggplot(aes(y = "", x = value, fill = `trial type`)) +
stat_halfeye(alpha = .5) +
scale_fill_manual(values = c(palette[2], palette[1])) +
scale_y_discrete(expand = c(.1, .1)) +
coord_cartesian(xlim = c(1.5, 2.5)) +
labs(x = "\npredicted craving rating\n", y = "") +
plot_aes
behavior_mindfulness %>%
spread_draws(b_Intercept, b_trial_condregulation) %>%
mutate(reactivity = b_Intercept,
mindfulness = b_Intercept + b_trial_condregulation) %>%
gather(`trial type`, value, reactivity, mindfulness) %>%
filter(`trial type` == "reactivity") %>%
ggplot(aes(y = "", x = value, fill = `trial type`)) +
stat_halfeye(alpha = .5) +
scale_fill_manual(values = c(palette[1], palette[1])) +
scale_y_discrete(expand = c(.1, .1)) +
coord_cartesian(xlim = c(1.5, 2.5)) +
labs(x = "\npredicted craving rating\n", y = "") +
plot_aes
table
make_table(behavior_mindfulness)| term | b [95% CI] |
|---|---|
| intercept | 2.00 [1.76, 2.23] |
| trial condition (regulation) | -0.12 [-0.23, -0.01] |
summary
summary(behavior_mindfulness)## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: resp ~ 1 + trial_cond + (1 + trial_cond | pID) + (1 | stimulus)
## Data: disaggregated_mindfulness (Number of observations: 2214)
## Draws: 4 chains, each with iter = 1000; warmup = 500; thin = 1;
## total post-warmup draws = 2000
##
## Group-Level Effects:
## ~pID (Number of levels: 37)
## Estimate Est.Error l-95% CI u-95% CI Rhat
## sd(Intercept) 0.60 0.08 0.47 0.77 1.00
## sd(trial_condregulation) 0.21 0.07 0.06 0.34 1.00
## cor(Intercept,trial_condregulation) -0.09 0.27 -0.53 0.53 1.00
## Bulk_ESS Tail_ESS
## sd(Intercept) 457 713
## sd(trial_condregulation) 385 225
## cor(Intercept,trial_condregulation) 1407 521
##
## ~stimulus (Number of levels: 72)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.39 0.04 0.32 0.47 1.01 631 1005
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## Intercept 2.00 0.12 1.76 2.23 1.01 289
## trial_condregulation -0.12 0.05 -0.23 -0.01 1.00 1953
## Tail_ESS
## Intercept 641
## trial_condregulation 1717
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 0.85 0.01 0.82 0.87 1.00 3292 1564
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).craving: mindfulness signature analyses
H2 & H3
H2:
We expect that people who have greater expression of the mindfulness signature on average (i.e., L2, between-person expression) will also have lower craving ratings on a trial-by-trial basis.
Results
✅ There is a statistically significant interaction between trial type and signature expression, such that people who have higher signature expression on average during mindfulness trials also report lower craving ratings, compared to during reactivity trials.
H3:
H3: We expect that trials with greater expression of the mindfulness signature compared to one’s average (i.e., L1, within-person expression) will be associated with lower craving ratings on a trial-by-trial basis.
Results
❌ Directionally, greater signature expression compared to one’s mean is associated with lower craving ratings on mindfulness trials, but this relationship is small and not statistically significant.
visualize raw data
averaged within participant
merged %>%
filter(condition == "mindfulness") %>%
group_by(pID, trial_cond) %>%
mutate(mean_craving = mean(resp, na.rm = TRUE),
mean_expression = mean(dot, na.rm = TRUE)) %>%
select(pID, trial_cond, mean_expression, mean_craving) %>%
unique() %>%
ggplot(aes(mean_expression, mean_craving, color = trial_cond, fill = trial_cond)) +
geom_jitter(alpha = .5, height = .1) +
geom_smooth(method = "lm") +
scale_color_manual(name = "trial type", values = palette) +
scale_fill_manual(name = "trial type", values = palette) +
labs(x = expression("\nreactivity " * symbol('\254') * " mean signature expression " * symbol('\256') * " mindfulness"),
y = "mean craving rating\n") +
plot_aes +
theme(legend.position = "top")
all data
merged %>%
filter(condition == "mindfulness") %>%
group_by(pID, trial_cond) %>%
select(pID, trial_cond, dot, resp) %>%
unique() %>%
ggplot(aes(dot, resp, color = trial_cond, fill = trial_cond)) +
geom_jitter(alpha = .5, height = .1) +
geom_smooth(method = "lm") +
scale_color_manual(name = "trial type", values = palette) +
scale_fill_manual(name = "trial type", values = palette) +
labs(x = expression("\nreactivity " * symbol('\254') * " mean signature expression " * symbol('\256') * " mindfulness"),
y = "mean craving rating\n") +
plot_aes +
theme(legend.position = "top")
participant slopes
disaggregated_mindfulness %>%
ggplot(aes(dot, resp, color = trial_cond, fill = trial_cond)) +
geom_smooth(aes(group = interaction(pID, trial_cond)), method = "lm", se = FALSE, size = .2) +
geom_smooth(method = "lm") +
scale_color_manual(name = "trial type", values = palette) +
scale_fill_manual(name = "trial type", values = palette) +
labs(x = expression("\nreactivity " * symbol('\254') * " signature expression " * symbol('\256') * " mindfulness"),
y = "craving rating\n") +
plot_aes +
theme(legend.position = "top")
run model
plots
between-person only
regulation
points_between = disaggregated_mindfulness %>%
select(pID, resp, trial_cond, dot_between_std_noc) %>%
group_by(pID, trial_cond, dot_between_std_noc) %>%
summarize(resp = mean(resp, na.rm = TRUE)) %>%
mutate(group = ifelse(trial_cond == "regulation", "mindfulness", "reactivity"),
group = factor(group, levels = c("reactivity", "mindfulness")),
type = "between-person") %>%
rename("x" = dot_between_std_noc,
"predicted" = resp)
vals = seq(-2,2,.2)
predicted = ggeffects::ggpredict(mod_h2_int, c("dot_between_std_noc [vals]", "trial_cond"), type = "fe") %>%
data.frame() %>%
mutate(type = "between-person") %>%
mutate(group = ifelse(group == "regulation", "mindfulness", "reactivity"),
group = factor(group, levels = c("reactivity", "mindfulness")))
predicted %>%
filter(group == "mindfulness") %>%
ggplot(aes(x, predicted, color = group, fill = group)) +
geom_point(data = filter(points_between, group == "mindfulness"), alpha = .4, size = 2) +
geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = .2, color = NA) +
geom_line(size = 1) +
scale_fill_manual(name = "trial type", values = palette[2]) +
scale_color_manual(name = "trial type", values = palette[2]) +
coord_cartesian(ylim = c(.5, 4)) +
labs(x = expression("\nreactivity " * symbol('\254') * " signature expression " * symbol('\256') * " mindfulness"),
y = "predicted craving rating\n") +
plot_aes +
theme(legend.position = "top")
predicted %>%
filter(group == "mindfulness") %>%
ggplot(aes(x, predicted, color = group, fill = group)) +
geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = .2, color = NA) +
geom_line(size = 1) +
scale_fill_manual(name = "trial type", values = palette[2]) +
scale_color_manual(name = "trial type", values = palette[2]) +
coord_cartesian(ylim = c(.5, 4)) +
labs(x = expression("\nreactivity " * symbol('\254') * " signature expression " * symbol('\256') * " mindfulness"),
y = "predicted craving rating\n") +
plot_aes +
theme(legend.position = "top")
regulation and reactivity
predicted %>%
ggplot(aes(x, predicted, color = group, fill = group)) +
geom_point(data = points_between, alpha = .4, size = 2) +
geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = .2, color = NA) +
geom_line(size = 1) +
scale_fill_manual(name = "trial type", values = palette) +
scale_color_manual(name = "trial type", values = palette) +
coord_cartesian(ylim = c(.5, 4)) +
labs(x = expression("\nreactivity " * symbol('\254') * " signature expression " * symbol('\256') * " mindfulness"),
y = "predicted craving rating\n") +
plot_aes +
theme(legend.position = "top")
predicted %>%
ggplot(aes(x, predicted, color = group, fill = group)) +
geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = .2, color = NA) +
geom_line(size = 1) +
scale_fill_manual(name = "trial type", values = palette) +
scale_color_manual(name = "trial type", values = palette) +
coord_cartesian(ylim = c(.5, 4)) +
labs(x = expression("\nreactivity " * symbol('\254') * " signature expression " * symbol('\256') * " mindfulness"),
y = "predicted craving rating\n") +
plot_aes +
theme(legend.position = "top")
within-person only
points_within = disaggregated_mindfulness %>%
select(pID, resp, trial_cond, dot_within_std) %>%
mutate(group = ifelse(trial_cond == "regulation", "mindfulness", "reactivity"),
group = factor(group, levels = c("reactivity", "mindfulness")),
type = "within-person") %>%
rename("x" = dot_within_std,
"predicted" = resp) %>%
filter(x < 2 & x > -2)
vals = seq(-2,2,.2)
predicted = ggeffects::ggpredict(mod_h2_int, c("dot_within_std [vals]", "trial_cond"), type = "fe") %>%
data.frame() %>%
mutate(group = ifelse(group == "regulation", "mindfulness", "reactivity"),
group = factor(group, levels = c("reactivity", "mindfulness")))
predicted %>%
ggplot(aes(x, predicted, color = group, fill = group)) +
geom_point(data = points_within, alpha = .4, size = 2, position = position_jitter(.1,.1)) +
geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = .2, color = NA) +
geom_line(size = 1) +
scale_fill_manual(name = "trial type", values = palette) +
scale_color_manual(name = "trial type", values = palette) +
coord_cartesian(ylim = c(.5, 3)) +
labs(x = expression("\nreactivity " * symbol('\254') * " signature expression " * symbol('\256') * " mindfulness"),
y = "predicted craving rating\n") +
plot_aes +
theme(legend.position = "top")
table
make_table(mod_h2_int)| term | b [95% CI] |
|---|---|
| intercept | 2.29 [1.97, 2.61] |
| trial condition (regulation) | -0.15 [-0.52, 0.22] |
| signature expression (between) | 0.28 [0.07, 0.51] |
| signature expression (within) | -0.01 [-0.07, 0.05] |
| trial condition (regulation) x signature expression (between) | -0.57 [-0.94, -0.22] |
| trial condition (regulation) x signature expression (within) | -0.02 [-0.10, 0.06] |
simple slopes
emmeans::emtrends(mod_h2_int, ~ trial_cond, var="dot_between_std_noc") %>%
data.frame() %>%
mutate(`b [95% CI]` = sprintf("%.2f [%.2f, %.2f]", dot_between_std_noc.trend, lower.HPD, upper.HPD)) %>%
select(trial_cond, `b [95% CI]`)emmeans::emtrends(mod_h2_int, ~ trial_cond, var="dot_within_std") %>%
data.frame() %>%
mutate(`b [95% CI]` = sprintf("%.2f [%.2f, %.2f]", dot_within_std.trend, lower.HPD, upper.HPD)) %>%
select(trial_cond, `b [95% CI]`)summary
summary(mod_h2_int)## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: resp ~ 1 + trial_cond * dot_between_std_noc + trial_cond * dot_within_std + (1 + dot_within_std | pID) + (1 | stimulus)
## Data: disaggregated_mindfulness (Number of observations: 2213)
## Draws: 4 chains, each with iter = 1000; warmup = 500; thin = 1;
## total post-warmup draws = 2000
##
## Group-Level Effects:
## ~pID (Number of levels: 37)
## Estimate Est.Error l-95% CI u-95% CI Rhat
## sd(Intercept) 0.59 0.08 0.46 0.79 1.02
## sd(dot_within_std) 0.05 0.03 0.00 0.11 1.00
## cor(Intercept,dot_within_std) 0.06 0.42 -0.79 0.87 1.00
## Bulk_ESS Tail_ESS
## sd(Intercept) 193 419
## sd(dot_within_std) 462 791
## cor(Intercept,dot_within_std) 1598 905
##
## ~stimulus (Number of levels: 72)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.39 0.04 0.32 0.47 1.00 491 673
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI
## Intercept 2.29 0.16 1.97 2.61
## trial_condregulation -0.15 0.19 -0.52 0.22
## dot_between_std_noc 0.28 0.11 0.07 0.51
## dot_within_std -0.01 0.03 -0.07 0.05
## trial_condregulation:dot_between_std_noc -0.57 0.18 -0.94 -0.22
## trial_condregulation:dot_within_std -0.02 0.04 -0.10 0.06
## Rhat Bulk_ESS Tail_ESS
## Intercept 1.02 298 662
## trial_condregulation 1.00 981 1321
## dot_between_std_noc 1.00 866 1309
## dot_within_std 1.00 1289 1178
## trial_condregulation:dot_between_std_noc 1.00 1190 1116
## trial_condregulation:dot_within_std 1.00 1489 1362
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 0.85 0.01 0.82 0.88 1.00 2313 1562
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).planned exploratory task analyses
E1
To examine divergent validity, we will apply the mindfulness signature to data from a separate group of participants who were instructed to use a different form of cognitive regulation (i.e., not mindfulness) that is not expected to rely on the same brain regions. We expect lower than chance accuracy decoding alcohol regulation versus reactivity trials.
I haven’t done what we laid out in our prereg yet, but here are the same analyses from H1b for the perspective taking group. The classifier is much less accurate in this group (accuracy = 54%) than in the mindfulness group.
means
plot
data_means = merged %>%
filter(condition == "perspective")
data_means %>%
ggplot(aes(trial_cond, dot, color = trial_cond, fill = trial_cond)) +
stat_summary(fun.data = "mean_cl_boot", geom = "bar") +
stat_summary(fun.data = "mean_cl_boot", geom = "errorbar", width = 0, color = "black") +
scale_color_manual(name = "", values = palette) +
scale_fill_manual(name = "", values = palette) +
labs(x = "\ntrial type", y = "signature expression (dot product)\n") +
plot_aes +
theme(legend.position = "none")
run model
mod_e1 = brms::brm(dot ~ trial_cond + (1 | pID), data = data_means,
cores = 8, iter = 1000, silent = TRUE, seed = 6523)## Running /Library/Frameworks/R.framework/Resources/bin/R CMD SHLIB foo.c
## clang -mmacosx-version-min=10.13 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I"/Library/Frameworks/R.framework/Versions/4.1/Resources/library/Rcpp/include/" -I"/Library/Frameworks/R.framework/Versions/4.1/Resources/library/RcppEigen/include/" -I"/Library/Frameworks/R.framework/Versions/4.1/Resources/library/RcppEigen/include/unsupported" -I"/Library/Frameworks/R.framework/Versions/4.1/Resources/library/BH/include" -I"/Library/Frameworks/R.framework/Versions/4.1/Resources/library/StanHeaders/include/src/" -I"/Library/Frameworks/R.framework/Versions/4.1/Resources/library/StanHeaders/include/" -I"/Library/Frameworks/R.framework/Versions/4.1/Resources/library/RcppParallel/include/" -I"/Library/Frameworks/R.framework/Versions/4.1/Resources/library/rstan/include" -DEIGEN_NO_DEBUG -DBOOST_DISABLE_ASSERTS -DBOOST_PENDING_INTEGER_LOG2_HPP -DSTAN_THREADS -DBOOST_NO_AUTO_PTR -include '/Library/Frameworks/R.framework/Versions/4.1/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp' -D_REENTRANT -DRCPP_PARALLEL_USE_TBB=1 -I/usr/local/include -fPIC -Wall -g -O2 -c foo.c -o foo.o
## In file included from <built-in>:1:
## In file included from /Library/Frameworks/R.framework/Versions/4.1/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
## In file included from /Library/Frameworks/R.framework/Versions/4.1/Resources/library/RcppEigen/include/Eigen/Dense:1:
## In file included from /Library/Frameworks/R.framework/Versions/4.1/Resources/library/RcppEigen/include/Eigen/Core:88:
## /Library/Frameworks/R.framework/Versions/4.1/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:628:1: error: unknown type name 'namespace'
## namespace Eigen {
## ^
## /Library/Frameworks/R.framework/Versions/4.1/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:628:16: error: expected ';' after top level declarator
## namespace Eigen {
## ^
## ;
## In file included from <built-in>:1:
## In file included from /Library/Frameworks/R.framework/Versions/4.1/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
## In file included from /Library/Frameworks/R.framework/Versions/4.1/Resources/library/RcppEigen/include/Eigen/Dense:1:
## /Library/Frameworks/R.framework/Versions/4.1/Resources/library/RcppEigen/include/Eigen/Core:96:10: fatal error: 'complex' file not found
## #include <complex>
## ^~~~~~~~~
## 3 errors generated.
## make: *** [foo.o] Error 1table
make_table(mod_e1)| term | b [95% CI] |
|---|---|
| intercept | -2.25 [-4.08, -0.51] |
| trial condition (regulation) | 1.97 [0.74, 3.10] |
summary
summary(mod_e1)## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: dot ~ trial_cond + (1 | pID)
## Data: data_means (Number of observations: 1606)
## Draws: 4 chains, each with iter = 1000; warmup = 500; thin = 1;
## total post-warmup draws = 2000
##
## Group-Level Effects:
## ~pID (Number of levels: 34)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 4.60 0.69 3.38 6.16 1.00 701 741
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## Intercept -2.25 0.88 -4.08 -0.51 1.00 679
## trial_condregulation 1.97 0.60 0.74 3.10 1.00 3159
## Tail_ESS
## Intercept 798
## trial_condregulation 1058
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 12.35 0.22 11.93 12.79 1.00 4372 1471
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).ROC
acc_line1 = data.frame(x = c(0, 1), y = c(1, 1))
acc_line2 = data.frame(y = c(0, 1), x = c(0, 0))
merged %>%
filter(condition %in% c("perspective", "mindfulness")) %>%
filter(!is.na(dot)) %>%
mutate(actual = ifelse(trial_cond == "regulation", 1, 0)) %>%
group_by(condition) %>%
do({
condition = .$condition
pred = prediction(.$dot, .$actual)
perf = performance(pred, measure = "tpr", x.measure = "fpr")
data.frame(cut = perf@alpha.values[[1]],fpr = perf@x.values[[1]],tpr = perf@y.values[[1]])
}) %>%
ggplot(aes(fpr, tpr)) +
geom_line(aes(color = condition), size = 1) +
geom_abline(intercept = 0, slope = 1) +
geom_line(data = acc_line1, aes(x, y)) +
geom_line(data = acc_line2, aes(x, y)) +
scale_x_continuous(breaks = seq(0, 1, .2)) +
scale_y_continuous(breaks = seq(0, 1, .2)) +
scale_color_manual(name = "", values = palette_group) +
labs(x = "\nfalse positive rate (1 - specificity)", y = "true positive rate (sensitivity)\n") +
plot_aes +
theme(legend.position = c(.8, .3),
legend.spacing.y = unit(-.1, "cm"))
confusion matrix
conf_data = merged %>%
filter(condition == "perspective") %>%
filter(!is.na(dot)) %>%
mutate(predicted = ifelse(dot > 0, "regulation",
ifelse(dot < 0, "reactivity", NA)),
predicted = as.factor(predicted),
trial_cond = as.factor(as.character(trial_cond)))
caret::confusionMatrix(conf_data$predicted, conf_data$trial_cond)## Confusion Matrix and Statistics
##
## Reference
## Prediction reactivity regulation
## reactivity 468 417
## regulation 336 385
##
## Accuracy : 0.5311
## 95% CI : (0.5064, 0.5558)
## No Information Rate : 0.5006
## P-Value [Acc > NIR] : 0.007736
##
## Kappa : 0.0621
##
## Mcnemar's Test P-Value : 0.003553
##
## Sensitivity : 0.5821
## Specificity : 0.4800
## Pos Pred Value : 0.5288
## Neg Pred Value : 0.5340
## Prevalence : 0.5006
## Detection Rate : 0.2914
## Detection Prevalence : 0.5511
## Balanced Accuracy : 0.5311
##
## 'Positive' Class : reactivity
## E2
We will explore whether expression of the mindfulness signature varies naturally during alcohol cue reactivity among participants not instructed to use mindfulness, and the degree to which expression is related to cravings.
Results
Between-person: ✅ ❌ People with stronger expression of the mindfulness signature on average also report lower craving ratings during exposure to alcohol cues. However, the credible interval includes zero.
Within-person: ✅ Stronger expression of the mindfulness signature compared to one’s average is associated with lower craving ratings during exposure to alcohol cues.
run model
mod_e2 = brms::brm(resp ~ dot_between_std_noc + dot_within_std +
(1 + dot_within_std | pID) + (1 | stimulus), data = disaggregated_control,
cores = 8, iter = 1000, silent = TRUE, seed = 6523)## Running /Library/Frameworks/R.framework/Resources/bin/R CMD SHLIB foo.c
## clang -mmacosx-version-min=10.13 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I"/Library/Frameworks/R.framework/Versions/4.1/Resources/library/Rcpp/include/" -I"/Library/Frameworks/R.framework/Versions/4.1/Resources/library/RcppEigen/include/" -I"/Library/Frameworks/R.framework/Versions/4.1/Resources/library/RcppEigen/include/unsupported" -I"/Library/Frameworks/R.framework/Versions/4.1/Resources/library/BH/include" -I"/Library/Frameworks/R.framework/Versions/4.1/Resources/library/StanHeaders/include/src/" -I"/Library/Frameworks/R.framework/Versions/4.1/Resources/library/StanHeaders/include/" -I"/Library/Frameworks/R.framework/Versions/4.1/Resources/library/RcppParallel/include/" -I"/Library/Frameworks/R.framework/Versions/4.1/Resources/library/rstan/include" -DEIGEN_NO_DEBUG -DBOOST_DISABLE_ASSERTS -DBOOST_PENDING_INTEGER_LOG2_HPP -DSTAN_THREADS -DBOOST_NO_AUTO_PTR -include '/Library/Frameworks/R.framework/Versions/4.1/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp' -D_REENTRANT -DRCPP_PARALLEL_USE_TBB=1 -I/usr/local/include -fPIC -Wall -g -O2 -c foo.c -o foo.o
## In file included from <built-in>:1:
## In file included from /Library/Frameworks/R.framework/Versions/4.1/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
## In file included from /Library/Frameworks/R.framework/Versions/4.1/Resources/library/RcppEigen/include/Eigen/Dense:1:
## In file included from /Library/Frameworks/R.framework/Versions/4.1/Resources/library/RcppEigen/include/Eigen/Core:88:
## /Library/Frameworks/R.framework/Versions/4.1/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:628:1: error: unknown type name 'namespace'
## namespace Eigen {
## ^
## /Library/Frameworks/R.framework/Versions/4.1/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:628:16: error: expected ';' after top level declarator
## namespace Eigen {
## ^
## ;
## In file included from <built-in>:1:
## In file included from /Library/Frameworks/R.framework/Versions/4.1/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
## In file included from /Library/Frameworks/R.framework/Versions/4.1/Resources/library/RcppEigen/include/Eigen/Dense:1:
## /Library/Frameworks/R.framework/Versions/4.1/Resources/library/RcppEigen/include/Eigen/Core:96:10: fatal error: 'complex' file not found
## #include <complex>
## ^~~~~~~~~
## 3 errors generated.
## make: *** [foo.o] Error 1plot
predicted = ggeffects::ggpredict(mod_e2, c("dot_between_std_noc [-2:2]"), type = "fe") %>%
data.frame() %>%
mutate(type = "between-person") %>%
bind_rows(ggeffects::ggpredict(mod_e2, c("dot_within_std [-2:2]"), type = "fe") %>%
data.frame() %>%
mutate(type = "within-person"))
predicted %>%
ggplot(aes(x, predicted, color = type, fill = type)) +
geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = .2, color = NA) +
geom_line(size = 1) +
scale_fill_manual(name = "", values = palette[3:4]) +
scale_color_manual(name = "", values = palette[3:4]) +
labs(y = "predicated craving rating\n", x = "\nsignature expression (SD)") +
plot_aes +
theme(legend.position = "top")
table
make_table(mod_e2)| term | b [95% CI] |
|---|---|
| intercept | 1.88 [1.65, 2.11] |
| signature expression (between) | -0.19 [-0.40, 0.01] |
| signature expression (within) | -0.07 [-0.13, -0.02] |
summary
summary(mod_e2)## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: resp ~ dot_between_std_noc + dot_within_std + (1 + dot_within_std | pID) + (1 | stimulus)
## Data: disaggregated_control (Number of observations: 1785)
## Draws: 4 chains, each with iter = 1000; warmup = 500; thin = 1;
## total post-warmup draws = 2000
##
## Group-Level Effects:
## ~pID (Number of levels: 39)
## Estimate Est.Error l-95% CI u-95% CI Rhat
## sd(Intercept) 0.63 0.08 0.50 0.81 1.01
## sd(dot_within_std) 0.11 0.03 0.05 0.18 1.00
## cor(Intercept,dot_within_std) -0.07 0.30 -0.66 0.49 1.00
## Bulk_ESS Tail_ESS
## sd(Intercept) 553 719
## sd(dot_within_std) 723 777
## cor(Intercept,dot_within_std) 1384 1150
##
## ~stimulus (Number of levels: 72)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.28 0.03 0.22 0.36 1.00 836 1313
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept 1.88 0.12 1.65 2.11 1.01 292 628
## dot_between_std_noc -0.19 0.11 -0.40 0.01 1.01 557 1078
## dot_within_std -0.07 0.03 -0.13 -0.02 1.00 2295 1773
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 0.88 0.01 0.85 0.91 1.00 3013 1518
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).E3
We will also explore the degree to which variability in mindfulness throughout the task is related to alcohol cravings on average. We expect that participants who more consistently express the mindfulness signature also report lower craving over the task.
Results
✅ ❌ Collapsed across trial types, higher variability in expression of the mindfulness signature is related to higher cravings. However, the credible interval includes zero.
run model
e3_data = merged %>%
filter(condition == "mindfulness") %>%
group_by(pID, trial_cond, trial_cond_recode) %>%
mutate(dot_sd = sd(dot, na.rm = TRUE),
mean_expression = mean(dot, na.rm = TRUE))
mod_e3 = brms::brm(resp ~ trial_cond_recode * dot_sd + mean_expression +
(1 | pID) + (1 | stimulus), data = e3_data,
cores = 8, iter = 1000, silent = TRUE, seed = 6523)## Running /Library/Frameworks/R.framework/Resources/bin/R CMD SHLIB foo.c
## clang -mmacosx-version-min=10.13 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I"/Library/Frameworks/R.framework/Versions/4.1/Resources/library/Rcpp/include/" -I"/Library/Frameworks/R.framework/Versions/4.1/Resources/library/RcppEigen/include/" -I"/Library/Frameworks/R.framework/Versions/4.1/Resources/library/RcppEigen/include/unsupported" -I"/Library/Frameworks/R.framework/Versions/4.1/Resources/library/BH/include" -I"/Library/Frameworks/R.framework/Versions/4.1/Resources/library/StanHeaders/include/src/" -I"/Library/Frameworks/R.framework/Versions/4.1/Resources/library/StanHeaders/include/" -I"/Library/Frameworks/R.framework/Versions/4.1/Resources/library/RcppParallel/include/" -I"/Library/Frameworks/R.framework/Versions/4.1/Resources/library/rstan/include" -DEIGEN_NO_DEBUG -DBOOST_DISABLE_ASSERTS -DBOOST_PENDING_INTEGER_LOG2_HPP -DSTAN_THREADS -DBOOST_NO_AUTO_PTR -include '/Library/Frameworks/R.framework/Versions/4.1/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp' -D_REENTRANT -DRCPP_PARALLEL_USE_TBB=1 -I/usr/local/include -fPIC -Wall -g -O2 -c foo.c -o foo.o
## In file included from <built-in>:1:
## In file included from /Library/Frameworks/R.framework/Versions/4.1/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
## In file included from /Library/Frameworks/R.framework/Versions/4.1/Resources/library/RcppEigen/include/Eigen/Dense:1:
## In file included from /Library/Frameworks/R.framework/Versions/4.1/Resources/library/RcppEigen/include/Eigen/Core:88:
## /Library/Frameworks/R.framework/Versions/4.1/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:628:1: error: unknown type name 'namespace'
## namespace Eigen {
## ^
## /Library/Frameworks/R.framework/Versions/4.1/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:628:16: error: expected ';' after top level declarator
## namespace Eigen {
## ^
## ;
## In file included from <built-in>:1:
## In file included from /Library/Frameworks/R.framework/Versions/4.1/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
## In file included from /Library/Frameworks/R.framework/Versions/4.1/Resources/library/RcppEigen/include/Eigen/Dense:1:
## /Library/Frameworks/R.framework/Versions/4.1/Resources/library/RcppEigen/include/Eigen/Core:96:10: fatal error: 'complex' file not found
## #include <complex>
## ^~~~~~~~~
## 3 errors generated.
## make: *** [foo.o] Error 1plot
predicted = ggeffects::ggpredict(mod_e3, c("dot_sd", "trial_cond_recode"), type = "fe") %>%
data.frame() %>%
mutate(group = ifelse(group == .5, "mindfulness", "reactivity"),
group = factor(group, levels = c("reactivity", "mindfulness")))
predicted %>%
ggplot(aes(x, predicted, color = group, fill = group)) +
geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = .2, color = NA) +
geom_line(aes(group = group), size = 1) +
scale_fill_manual(name = "trial type", values = palette) +
scale_color_manual(name = "trial type", values = palette) +
labs(y = "predicated craving rating\n", x = "\nsignature expression variability") +
plot_aes +
theme(legend.position = "top")
table
make_table(mod_e3)| term | b [95% CI] |
|---|---|
| intercept | 1.65 [1.25, 2.06] |
| trial condition (regulation) | -0.31 [-0.81, 0.19] |
| signature expression variability | 0.02 [-0.01, 0.05] |
| mean signature expression | 0.01 [-0.02, 0.04] |
| trial condition (regulation) x signature expression variability | 0.01 [-0.02, 0.03] |
summary
summary(mod_e3)## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: resp ~ trial_cond_recode * dot_sd + mean_expression + (1 | pID) + (1 | stimulus)
## Data: e3_data (Number of observations: 2214)
## Draws: 4 chains, each with iter = 1000; warmup = 500; thin = 1;
## total post-warmup draws = 2000
##
## Group-Level Effects:
## ~pID (Number of levels: 37)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.58 0.08 0.45 0.76 1.01 289 469
##
## ~stimulus (Number of levels: 72)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.39 0.04 0.32 0.48 1.01 360 717
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## Intercept 1.65 0.21 1.25 2.06 1.02 440
## trial_cond_recode -0.31 0.26 -0.81 0.19 1.00 1004
## dot_sd 0.02 0.01 -0.01 0.05 1.00 683
## mean_expression 0.01 0.01 -0.02 0.04 1.00 1041
## trial_cond_recode:dot_sd 0.01 0.01 -0.02 0.03 1.00 1811
## Tail_ESS
## Intercept 866
## trial_cond_recode 1271
## dot_sd 811
## mean_expression 1134
## trial_cond_recode:dot_sd 1533
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 0.85 0.01 0.83 0.88 1.00 2618 1610
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).